基于时空动态约束图反馈的交通流预测

doi:10.13229/j.cnki.jdxbgxb.20240640

Abstract

Abstract:

Aiming at the problem of insufficient consideration of the time-varying characteristics of hidden spatial associations of road network nodes in the existing traffic flow prediction studies， this paper proposes a traffic flow prediction model based on the feedback of spatio-temporal dynamic constraint graph. First， the temporal features are extracted by GRU， generates a dynamic constraint graph characterising the neighbourhood relationship of the road network at the current moment by using a spatio-temporal graph generator and a spatio-temporal fusion constraint matrix within the STC-GCL component， and then realises spatial feature extraction by using a multilayer graph structure convolution operation. Second， the multi-scale gated convolution unit is used to dynamically adjust the information flow of important features to complete the fine screening of key features. Finally， the consistent extraction of spatio-temporal features is achieved by embedding STC-GCL into GRU. The experiments are tested on the public datasets of high-speed road network PeMSD4， PeMSD8， and Chengdu-DDT， and the results show that compared with the current mainstream spatio-temporal prediction methods for traffic flow FGI， the MAE of the proposed model in this paper reduced by 2.69%， 1.88%， and 0.92% in the three datasets， respectively.

Key words: traffic flow prediction, spatio-temporal nature, dynamic nature, graph convolutional neural network

CLC Number:

U491.1

Yue HOU,Xin ZHANG,Yue WU. Traffic flow prediction based on spatio-temporal dynamic constraint graph feedback[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(1): 183-198.

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数据集	地区	节点	日期	长度
PeMSD4	旧金山湾区	307	2018年1~2月	16 992
PeMSD8	圣贝纳迪诺区	170	2016年7~8月	17 856
成都-滴滴	成都	524	2018年1~4月	17 280

模型	PeMSD4			PeMSD8			成都-滴滴
模型	MAE	RMSE	R²	MAE	RMSE	R²	MAE	RMSE	R²
CNN-LSTM	20.69	35.05	0.920 1	16.41	26.63	0.926 4	2.32	3.51	0.693 3
ASTGCN	20.44	32.81	0.919 3	16.49	25.36	0.928 5	2.34	3.49	0.692 0
DCRNN	22.28	34.29	0.916 6	16.74	25.63	0.928 8	2.78	4.09	0.581 8
SCINet	20.41	32.48	0.960 0	16.69	26.15	0.938 0	2.25	3.45	0.690 5
STG-NCDE	20.29	31.99	0.960 0	15.82	24.89	0.960 8	2.32	3.44	0.693 2
DeepSTUQ	19.15	31.36	0.933 9	15.04	23.97	0.938 8	2.29	3.44	0.693 3
FGI	19.31	31.68	0.928 2	14.93	23.64	0.939 6	2.18	3.31	0.714 2
STC-GCL-MGRU	18.79	30.28	0.962 8	14.65	23.69	0.963 4	2.16	3.27	0.731 3

模型	5 min			30 min			60 min
模型	MAE	RMSE	R²	MAE	RMSE	R²	MAE	RMSE	R²
SCINet	14.49	22.17	0.940 1	18.12	28.32	0.926 5	19.81	31.14	0.910 4
STG-NCDE	14.22	21.79	0.948 0	17.38	27.44	0.928 7	19.27	30.36	0.912 3
DeepSTUQ	13.54	20.99	0.948 6	16.12	25.46	0.930 0	18.41	28.91	0.917 0
STC-GCL-MGRU	13.26	20.62	0.960 3	15.75	15.14	0.950 3	18.11	28.71	0.941 3

模型	PeMSD8
模型	MAE	RMSE	R²
GRU-GCL	23.09	33.29	0.950 0
STC-GCL-ADD-IG	17.64	28.39	0.961 4
STC-GCL-ADD-ST	17.19	27.79	0.963 1
STC-GCL-MGRU	14.65	23.69	0.963 4

模型	PeMSD8
模型	MAE	RMSE	R²
STC-GCL-MGRU-IG	20.23	31.93	0.951 0
STC-GCL-MGRU-ST	18.41	29.36	0.958 7
STC-GCL-MGRU-GCU	17.19	27.79	0.963 1
STC-GCL-MGRU	14.65	23.69	0.963 4

Traffic flow prediction based on spatio-temporal dynamic constraint graph feedback

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[2]	Xi-jun ZHANG,Guang-jie YU,Yong CUI,Ji-yang SHANG. Short-term traffic flow prediction based on clustering algorithm and graph neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(6): 1593-1600.
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